The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

Title: J-Space

Abstract: The rise of the term mechanistic interpretability has accompanied increasing interest in understanding neural models -- particularly language models. However, this jargon has also led to a fair amount of confusion. So, what does it mean to be mechanistic? We describe four uses of the term in interpretability research. The most narrow technical definition requires a claim of causality, while a broader technical definition allows for any exploration of a model's internals. However, the term also has a narrow cultural definition describing a cultural movement. To understand this semantic drift, we present a history of the NLP interpretability community and the formation of the separate, parallel mechanistic interpretability community. Finally, we discuss the broad cultural definition -- encompassing the entire field of interpretability -- and why the traditional NLP interpretability community has come to embrace it. We argue that the polysemy of mechanistic is the product of a critical divide within the interpretability community.

Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=ysKMaQx7Hp0lkq5nOb3zJ9ivNZVvLv.1&jst=2
CSE 656 Seminar in Computer Vision The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Chuntian Cao, CDS AID - Neural Network Potential (NNP) for Battery Electrolytes

Yeonju Go, NPP Physics - Generative AI for High-Energy Nuclear Physics

Gilchan Park, CDS AID - Graph RAG: Indexing, Retrieval and Generation

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Join CELT for a focused, one-hour overview of practical ways to help students use and evaluate AI thoughtfully, ethically, and in relation to disciplinary expectations without adding a separate AI unit. Faculty will adapt an existing activity, assignment, or discussion to include a focused AI-literacy component and leave with a short activity they can use or continue developing in Brightspace.

Register here for the Zoom session.

Abstract: Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods do not fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques on multiple metrics such as mean squared error (MSE), mean absolute error (MAE), and pearson correlation coefficient (PCC). Qualitative analysis establishes the effectiveness of MERGE in capturing cancer marker genes, thus consolidating its utility in diagnostics. As an extension of this work, we use MERGE in a setting with an uncertainty calibration branch to perform robust gene expression smoothing. We show that using patch-wise uncertainty from an uncertainty calibration model and the gene expression predictions from MERGE to enrich the ground truth gene expression matrix, results in better alignment with pathologist annotations, thus establishing that the smoothing is biologically informed.

Speaker: Aniruddha Ganguly

Location: Virtual Zoom Meeting


https://stonybrook.zoom.us/j/5474847973?pwd=Sng0Q2h1c1d3cm9sbFBmYUczMHZNdz09
Meeting ID: 547 484 7973
Passcode: 206739

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CSE 600 Seminar Series | Fall 2025

Abstract: Imagine machines that can see the invisible: drones locating wildfire survivors, cameras predicting building failures, and smartphones detecting skin tumors. These applications lie beyond today's vision systems, which focus only on human-visible information. In this talk, I argue that a wealth of scene information is hidden in light properties invisible to the human eye, such as the travel time of photons and polarization of light waves. I will present how co- designing camera hardware, graphics models, and learning algorithms unlocks these invisible properties to create superhuman vision systems. I will present three superhuman vision capabilities: seeing around blind corners, turning objects into cameras, and extracting internal stress fields. By analyzing faint light reflections on diffuse walls and shiny objects, we create virtual cameras that reveal scenes hidden from the line of sight - enabling autonomous systems to navigate safely. Using the polarization of light, we recover mechanical stress fields hidden inside objects - opening new possibilities for non-destructive material characterization. These capabilities point toward a future where machines can see the invisible: around us, beneath our bodies, and beyond our scientific understanding.

Bio:
Akshat Dave is an Assistant Professor in the Department of Computer Science at Stony Brook
University, USA. His research lies at the intersection of applied optics, computer vision, and
machine learning. His work has been recognized by Rice University's Best Thesis Award, Optica Best Paper Prize, SIGGRAPH Asia Doctoral Consortium, and fellowships by Qualcomm, Texas Instruments, and INK Global Foundation. Prior to Stony Brook, he was a Postdoctoral Associate at MIT Media Lab. He holds a Ph.D. from Rice University and a Masters and a Bachelors from Indian Institute of Technology Madras.
Abstract: Foundation models are rapidly becoming capable assistants for knowledge work, but their deployment in real settings is limited by three gaps: they do not transfer reliably across environments, their internal reasoning is opaque, and their behavior is hard to control precisely. In this talk, I argue that these limitations are not only about model size -- they are fundamentally about whether learning captures and leverages the underlying structure of the data-generating process. I use causal thinking as a practical lens to model what is invariant, what changes, and what can be intervened on, and I further show how this leads to learning principles that improve trustworthiness.
I will first present methods for learning unifying mechanisms from heterogeneous data, across domains and modalities, to enable reliable transfer and controllable generation. Next, I will show how structured concepts can be recovered even from seemingly unstructured data, by analyzing and improving self-supervised objectives (such as masking and diffusion) through hierarchical latent-variable models. These concept structures can then be used to interpret generative models and support targeted, multi-level edits. Finally, I connect these two threads to generalization beyond the training distribution. I will discuss natural conditions for extrapolation and a compositional generation framework that improves prompt following for novel concept combinations. I will conclude with a brief outlook on self-improving world models and AI-assisted scientific discovery.


Bio: Lingjing Kong is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon University. His research focuses on Causal AI for transferable, interpretable, and controllable systems, with an emphasis on understanding and exploiting the structure of real-world data to make foundation models actionable and more reliable. He develops identification principles and scalable algorithms for learning unified models from heterogeneous data, uncovering hierarchical concept structures in unstructured data (e.g., images and text), and generalizing beyond training support through compositionality and extrapolation. His work has appeared in top ML venues including ICML, NeurIPS, CVPR, ICLR, and EMNLP and has been prototyped and applied in industry.

Location: NCS 120